150+ Exercises - Data Structures in Python - Hands-On
What you'll learn
- Built-in data structures: list, tuple, set, dict, frozenset
- collections package: namedtuple, ChainMap, Counter, deque, defaultdict
- Implementations: Queue, Double-Ended Queue, Stacks
- Numpy arrays
- solve 150+ exercises with data structures in Python
- deal with real programming problems
- work with documentation and Stack Overflow
- guaranteed instructor support
Requirements
- completion of all courses in the Python Developer learning path
Description
The "150+ Exercises - Data Structures in Python - Hands-On" course is an in-depth, practical guide designed to provide learners with a comprehensive understanding of Python's core data structures. These include built-in types like lists, tuples, sets, dictionaries, and strings, as well as advanced structures such as stacks, queues, linked lists, trees, and graphs.
Each unit in this course is dedicated to a particular data structure with a series of hands-on exercises that challenge learners to solve problems using that particular data structure. These exercises range in complexity, catering to various skill levels and providing opportunities for learners to apply and consolidate their knowledge.
In addition, each exercise is accompanied by a thorough solution, giving learners the chance to review their work and understand different approaches to problem-solving. This approach reinforces learning and boosts confidence in handling Python's data structures.
This course is suitable for learners who have a basic understanding of Python and want to delve deeper into how data can be organized and manipulated within the language. Through practical, hands-on exercises, learners will gain the ability to select and implement the appropriate data structure for a given problem, an essential skill in software development and data analysis.
The "150+ Exercises - Data Structures in Python - Hands-On" course is the perfect choice for aspiring Python programmers, data scientists, or anyone seeking to enhance their problem-solving skills in Python.
Python - Code with Simplicity and Power!
Python is a versatile and powerful high-level programming language known for its simplicity and readability. It provides a clean and expressive syntax that emphasizes code readability, making it easier to write and understand compared to many other programming languages. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming, allowing developers to choose the most suitable approach for their projects.
Python boasts a vast standard library and a rich ecosystem of third-party packages, providing a wide range of tools and functionalities for diverse application domains. It is widely used in web development, scientific computing, data analysis, artificial intelligence, machine learning, automation, and more. Python's popularity stems from its ease of use, extensive community support, and cross-platform compatibility, enabling developers to write code once and run it on various operating systems.
With its beginner-friendly nature and extensive resources for learning, Python has become an excellent language for individuals starting their programming journey. Its simplicity and versatility make it an ideal choice for prototyping, rapid development, and solving complex problems with concise and elegant code. Python's philosophy of readability, combined with its broad application domain, has propelled it to be one of the most widely adopted programming languages across industries and disciplines.
Who this course is for:
- Python developers or programmers who want to strengthen their understanding and implementation of various data structures in Python
- students or individuals studying computer science or software engineering who want to gain practical experience with different data structures and their operations using Python
- programmers transitioning to Python from other programming languages who want to learn how to implement and utilize data structures in Python
- professionals working on projects that involve handling and managing data, who want to enhance their skills in choosing and using appropriate data structures in Python
- self-learners or enthusiasts interested in improving their knowledge of data structures and developing their programming skills using Python
- interview candidates preparing for technical interviews that require knowledge of data structures in Python
Instructor
- 4.4 Instructor Rating
- 9,837 Reviews
- 397,081 Students
- 171 Courses
EN
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
PL
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
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